Genomics Inform Search

CLOSE


Genomics Inform > Volume 19(4); 2021 > Article
Mazumder, Hasan, Rus'd, and Islam: In-silico characterization and structure-based functional annotation of a hypothetical protein from Campylobacter jejuni involved in propionate catabolism

Abstract

Campylobacter jejuni is one of the most prevalent organisms associated with foodborne illness across the globe causing campylobacteriosis and gastritis. Many proteins of C. jejuni are still unidentified. The purpose of this study was to determine the structure and function of a non-annotated hypothetical protein (HP) from C. jejuni. A number of properties like physiochemical characteristics, 3D structure, and functional annotation of the HP (accession No. CAG2129885.1) were predicted using various bioinformatics tools followed by further validation and quality assessment. Moreover, the protein-protein interactions and active site were obtained from the STRING and CASTp server, respectively. The hypothesized protein possesses various characteristics including an acidic pH, thermal stability, water solubility, and cytoplasmic distribution. While alpha-helix and random coil structures are the most prominent structural components of this protein, most of it is formed of helices and coils. Along with expected quality, the 3D model has been found to be novel. This study has identified the potential role of the HP in 2-methylcitric acid cycle and propionate catabolism. Furthermore, protein-protein interactions revealed several significant functional partners. The in-silico characterization of this protein will assist to understand its molecular mechanism of action better. The methodology of this study would also serve as the basis for additional research into proteomic and genomic data for functional potential identification.

Introduction

As a human diarrheal pathogen Campylobacter jejuni, a well-known gram-negative bacterium, was first identified in 1973 [1]. It has several features like thermophilic, microaerophilic, no fermenting, non-spore forming, motile, single flagellum properties [2]. C. jejuni is a common foodborne pathogen that causes acute gastroenteritis in people globally and is prevalent in developed countries [1,3]. The incidence of infection by C. jejuni is more frequent than the infections caused by other common species including Escherichia coli O157:H7, Salmonella and Shigella [4]. C. jejuni possesses remarkable distinctive biochemical features from other microbial species including alpha-hemolysis, catalase sensitivity, hippurate hydrolysis, and nitrate reduction [5].
The genome of C. jejuni is made up of 1,641,481 base pairs containing 1,707 genes which are predicted to encode 1,654 proteins [6]. The functions of several of these proteins are still unknown. Uncharacterized protein families and domains of unknown functions both include proteins with uncertain functions [7]. For these reasons, the research interest for several unknown proteins of C. jejuni has increased among biological researchers. These unknown proteins, originated from an open reading frame with no experimental evidence of translation, are termed as hypothetical proteins (HPs) due to lack of functional annotations [8].
Over the last few decades a revolution in computational biology has led to the development of numerous servers and tools to aid in the prediction of protein function. HPs that have unknown features can be identified by virtue of their homology to known proteins [7]. A number of bioinformatics tools including the CD Search Service, InterProScan have been designed to specify functions of HPs from many bacterial species [9]. Furthermore, the study of protein-protein interaction (PPI), which play an essential role during cellular processes, is crucial to understand the function of a protein in a biological network using software such as the STRING database [10]. Three-dimensional (3D) modeling, however, is also important to correlate structural knowledge with the function of undetermined proteins, through homology searches at the Protein Data Bank [11].
The aim of this study was to ascribe structural and biological function of the HP NVI_CJUN_00861 (accession No. CAG2129885.1) of C. jejuni, involved in catabolism of a short chain fatty acid (SCFA). Among SCFAs found within the gut, C. jejuni metabolizes only acetate and lactate [12]. Therefore, a protein involved in metabolism of a SCFA will provide insight about the metabolic flexibility of C. jejuni. A number of in-silico techniques were used to predict the physicochemical properties, phylogenetic information, subcellular distribution, secondary and 3D structure, active site location, functional properties, and PPI of the HP engaged in metabolism.

Methods

Sequence retrieval and phylogeny analysis

The amino acid sequence of the HP (accession No. CAG2129885) from the bacteria Campylobacter jejuni was retrieved as FASTA format from the NCBI protein database (https://www.ncbi.nlm.nih.gov). We have reviewed all bioinformatics tools and databases used in this study for functional annotation of HP (Table 1). To analyze the sequence similarity, BlastP [13] was used. The MUSCLE v3.6 [14] was used to conduct multiple sequence alignment and MEGA X [15] to phylogenetic analysis.

Physicochemical properties analysis

The ProtParam (http://web.expasy.org/protparam) [16] tool of the ExPASy server was used to analyze the physicochemical properties of the protein. The ProtParam tool computes various physicochemical properties such as molecular weight, theoretical isoelectric point (pI), composition of amino acid, total number of positive and negative residues, instability index, aliphatic index (AI), grand average of hydropathicity (GRAVY), molecular formula, and estimated half-life.

Subcellular localization identification

The subcellular localization was anticipated by utilizing the CELLO server (http://cello.life.nctu.edu.tw) [17]. The results were further cross-checked by using PSLpred (http://crdd.osdd.net/raghava/pslpred) [18] and PSORTb (https://www.psort.org/psortb) [19] servers which are used to predict subcellular localization of bacterial proteins.

Secondary structure prediction

The Self-Optimized Prediction Method with Alignment server- SOPMA (https://npsa-prabi.ibcp.fr/cgibin/npsa_automat.pl?page=npsa_sopma.html) [20] was used to predict the studied protein's secondary structure. The result was cross-checked by using PSI-blast based secondary structure predicting PSIPRED server (https://bioinf.cs.ucl.ac.uk/psipred) [21].

3D structure prediction and quality assessment

The 3D model of the protein was generated by the HHpred server (https://toolkit.tuebingen.mpg.de/tools/hhpred) [22]. The YASARA energy minimization server (http://www.yasara.org/minimizationserver.htm) [23] was utilized to increase the side-chain accuracy, physical realism, and stereochemistry in homology modeling. The PyMOL v2.0 [24] was used for structural analysis and model figure generation. The SAVES server (https://services.mbi.ucla.edu) was used to assess the HP's anticipated 3D structure model's reliability. The Ramachandran plot analysis [25] in PROCHECK was used to visualize the backbone dihedral angles ψ against φ of amino acid residues in the HP structure, Verify3D [26] to determine the compatibility of an atomic model (3D) with its amino acid sequence, and ERRAT [27] to cross-check the studied HP structure.

Functional annotation

To identify the conserved domain of the protein sequence, the Conserved Domain Search Service (CD Search) (https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) [28] from NCBI was used. The protein sequence analysis and classification server InterProScan (https://www.ebi.ac.uk/interpro/search/sequence) [29] was then used for the functional analysis of the protein.

Protein-protein interaction

PPIs are a never-ending, intricate web of reactions that are essential for the control and execution of most biological processes. A protein-protein functional interaction network was identified by the STRING v11.0 (https://string-db.org) [10] search.

Active site identification

The active site of the HP was identified by the Computed Atlas of Surface Topography of Protein (CASTp) (http://sts.bioengr.uic.edu/castp) [30] which is an online asset for finding, outlining, and estimating inward surface regions on protein 3D structure.

Performance assessment

A receiver operating characteristic (ROC) was carried out for randomly selected 40 proteins with known functions of C. jejuni (Supplementary Table 1) to confirm the accuracy of the predicted function of the HP using the same bioinformatics tools and databases that were used. We used two binary numerals “0” and “1” to classify the prediction as true positive (1) and true negative (0) whereas the integers (2, 3, 4, and 5) to evaluate the six levels diagnostic efficacy. The classification data were submitted to a web-based calculator to calculate the sensitivity, specificity, ROC area, and accuracy of the tools used to annotate the function of HP [31].

Results and Discussion

Sequence and similarity information

All information of the HP (accession No. CAG2129885) was collected from the NCBI database (Supplementary Table 2). BlastP was performed against the non-redundant protein sequences (nr) database and UniProtKB/Swiss-Prot (swissprot) database which showed demonstrated homology of the HP with other MmgE/PrpD family protein and cis-aconitate decarboxylase (CAD), respectively (Tables 2 and 3). A phylogenetic tree (Fig. 1) was constructed using the neighbor-joining method with a bootstrap replication of 1,000 to confirm the homology assessment between proteins.

Physicochemical features

The physicochemical properties of the studied protein (Supplementary Table 3) were obtained from the ExPASy ProtPram server illustrated that the protein contains 446 amino acids with a molecular weight of 49478.88 Da. Among the composition Ala (46), Ile (42), Leu (42), Lys (38), Phe (33), Ser (31), Asn (30), Glu (29), Gly (26), Asp (24), Val (17), Thr (14), Pro (14), His (13), Tyr (11), Gln (10), Cys (8), Met (8), Arg (7), and Trp (3) were most abundant. The number of negatively charged residues (Asp + Glu) and positively charged residues (Arg + Lys) was computed as 53 and 45, respectively. The pI was calculated as 5.93, which is an indicator that the protein is acidic (pH < 7) in character. The instability index was found to be 29.84 which classifies the HP as a stable protein. The AI was found to be 94.82 which implies the stability of the protein over a wide range of temperatures. The GRAVY score, ‒0.002, indicated that the protein is soluble in water (hydrophilic). The molecular formula of the HP was C2250H3490N574O649S16. The putative protein's half-life was estimated to be >20 h in yeast (in-vivo), >10 h in Escherichia coli (in-vivo), and 30 h in mammalian reticulocytes (in-vitro).

Subcellular localization

Since protein subcellular localization can provide information about a protein's function in an organism, computerized prediction of protein subcellular localization is an important technique for protein analysis and annotation. Subcellular localization involves the identification of the protein location within a cell. The protein functions are greatly influenced by their subcellular localization. Based on analysis of the CELLO server protein localization predictions, the HP was identified as a cytoplasmic protein. The PSORTb server also identified the protein as a cytoplasmic one with a high localization score (9.97). The PSLpred protein subcellular localization server similarly indicated the protein as a cytoplasmic one.

Secondary structure analysis

Protein function is highly conserved by its structure. A significant portion of the secondary structure of the protein is helix, sheet, turn, and coil. The secondary structure of the HP, obtained from SOPMA server, demonstrated that it was composed of the alpha helix (55.16%), random coil (33.41%), extended strand (7.17%), and beta-turn (4.26%) (Fig. 2). A similar result was found from the PSIPRED server (Fig. 3) validated the previous result.

3D structure analysis

The 3D structure of a protein is intimately connected to its functional activities. Homology modeling was used to obtain the 3D structure of the HP from HHpred. YASARA energy minimization server modified the model structure to a more stable one by reducing its energy from 11,240.6 kJ/mol to ‒219,800.0 kJ/mol. The 3D structure of the protein obtained from PyMOL (Fig. 4) was validated by PROCHECK’s Ramachandran plot analysis, Verify3D, and ERRAT. The Ramachandran plot analysis (Fig. 5A) revealed that the number of amino acids in the most favored region was 91.3% (Supplementary Table 4), which is an indicator of a valid model. An overall quality factor of 96.99 by ERRAT verified the model as good quality (Fig. 5B). Verify3D also proved the validity of the predicted model by showing that 86.52% of the residues have averaged 3D-1D score ≥ 0.2 (Fig. 5C).

Functional annotation

The conserved domain search service tool of NCBI had identified a functional domain located in the protein sequence of the HP. The domain that was found in the HP is of MmgE/PrpD family protein (accession No. pfam03972) which is involved in propionate catabolism. Under certain conditions, the breakdown of propionate results in the creation of propionyl-CoA, which is carboxylated to D-methylmalonyl-CoA, isomerized to L-methylmalonyl-CoA, and convertes to succinyl-CoA, which is supplied to various cellular processes [32]. Many bacteria can use propionate as their only carbon source. It has a close relationship with the malonate metabolic pathway and central metabolism [33].
The result was cross-checked by InterProScan and later validated by Pfam which produced the same result. Pfam server identified MmgE/PrpD N-terminal domain at 4-440 amino acid residues with an e-value of 3.8e-105. Additionally, to identify the accuracy of the tools and databases used to specify the function of the protein, ROC curve was calculated. An average accuracy for the used pipeline was found to be 96.7% and area under the curve was 0.99 (Table 4) indicating the high reliability of in-silico tools and databases used in this study.
Proteins belonging to the MmgE/PrpD family protein contain 2-methylcitrate dehydratase (PrpD; 4.2.1.79). The 2-methylcitric acid cycle catalyzed by PrpD leads to propionate catabolism. PrpD catalyzes the third step of the 2-methylcitric acid cycle [34,35]. This functional protein is made up of a broad domain with an all-helical fold and a smaller domain that folds into an alpha + beta domain [36]. CAD and MmgE/PrpD family protein share a lot of similarities. In Aspergillus terreus, CAD is needed for the production of itaconic acid [37]. It has been previously reported that citrate/2-methylcitrate dehydratase of Bacillus subtilis possesses both 2-methylcitrate dehydratase and citrate dehydratase and thus it is active in the tricarboxylic acid cycle and methylcitric acid processes [38].

PPI analysis

PPI network of the HP was obtained from STRING server (Fig. 6). Functional partners with their scores predicted by the STRING search were gltA (0.991), acnB (0.961), purB-2 (0.886), metC (0.857), EAQ72564.1 (0.811), EAQ72574.1 (0.810), lecC (0.595), EAQ72769.1 (0.535), guaB (0.529), and acs (0.478) (Supplementary Table 5).

Active site analysis

Protein’s active site is the region of its surface that facilitates its binding with a specific molecular substrate which then undergoes catalysis. The CASTp server had demonstrated that 14 amino acid residues were present in the active site of the protein (Fig. 7) and the best active site was in areas with 128.249 and a volume of 79.033. The residues in the active site were shown in Fig. 8.

Conclusion

Protein has a fundamental role in different biological processes, and all living things rely on it. The studied HP helps bacteria in propionate catabolism and influences the 2-methylcitric acid cycle. The basic knowledge on C. jejuni will be strengthened by these characters of the HP. However, the findings of the analyses show the validity of the bioinformatics tools and databases employed in this study, as well as the potential for extended in-vitro research on the HP.

Notes

Authors’ Contribution

Conceptualization: MAI. Data curation: LM. Formal analysis: LM, MH. Methodology: LM, MH. Writing - original draft: LM, MH. Writing - review & editing: AAR, MAI.

Conflicts of Interest

No potential conflict of interest relevant to this article was reported.

Supplementary Materials

Supplementary data can be found with this article online at http://www.genominfo.org.
Supplementary Table 1.
List of annotated functions of 40 proteins with known function from Campylobacter jejuni using BLAST, CD Search, and InterProScan for ROC analysis
gi-21043suppl1.pdf
Supplementary Table 2.
Hypothetical protein’s information collected from NCBI
gi-21043suppl2.pdf
Supplementary Table 3.
Physicochemical properties of the hypothetical protein NVI_CJUN_00861
gi-21043suppl3.pdf
Supplementary Table 4.
Ramachandran plot statistics of the hypothetical protein
gi-21043suppl4.pdf
Supplementary Table 5.
Functional partners and their functions of the hypothetical protein predicted by the STRING server
gi-21043suppl5.pdf

Fig. 1.
Phylogenetic relatedness of the study protein (indicated with a black diamond) along with similar other proteins obtained from non-redundant protein sequences (nr) database. Scale bars represents substitutions per nucleotide site. Evolutionary analyses were conducted in MEGA X using Jones-Taylor-Thornton model with 1,000 bootstraps.
gi-21043f1.jpg
Fig. 2.
Secondary structure model predicted by the SOPMA server.
gi-21043f2.jpg
Fig. 3.
Secondary structure model by PSIPRED server.
gi-21043f3.jpg
Fig. 4.
Predicted 3D structure of the hypothetical protein rendered by PyMOL.
gi-21043f4.jpg
Fig. 5.
3D model of the studied hypothetical protein of Campylobacter jejuni validated by Ramachandran plot of PROCHECK program (A), ERRAT (B) (value overall quality factor: 96.991 from the SAVES server), and Verify3D (C).
gi-21043f5.jpg
Fig. 6.
Protein-protein interaction network of the hypothetical protein from the STRING server. The colored nodes indicate the query proteins and the first shell of interactors, the white nodes indicate the second shell of interactors, the empty nodes represent proteins with an unknown three-dimensional structure, and the filled nodes represent proteins with a known or predicted three-dimensional structure.
gi-21043f6.jpg
Fig. 7.
Active site (indicated as red color) of the studied hypothetical protein.
gi-21043f7.jpg
Fig. 8.
The amino acid residues in the active site of the studied protein (blue color).
gi-21043f8.jpg
Table 1.
List of bioinformatics tools and databases used for sequence based function annotation
Sl Software Function References
A Sequence similarity search
1 BlastP Used to find similar sequences in protein databases [13]
2 MUSCLE Used to conduct multiple sequence alignment [14]
3 MEGA X Used for inferring phylogenetic trees [15]
B Physiochemical characterization
4 ExPASy-Protparam tool Used for computation of various physical and chemical parameters of protein [16]
C Sub-cellular localization
5 CELLO Assign localization to both prokaryotic and eukaryotic proteins [17]
6 PSLpred Used to predict subcellular localization of proteins from Gram-negative bacteria [18]
7 PSORTb Used to predict subcellular localization of bacterial proteins [19]
D Secondary structure prediction
8 SOPMA Used to predict the secondary structure of protein [20]
9 PSIPRED Used for predicting PSI-blast based secondary structure to analyze protein [21]
E 3D structure prediction and quality assessment
10 HHpred Used to detect protein homology by HMM-HMM comparison [22]
11 YASARA Utilized to increase the stability of the 3D model structure [23]
12 PyMOL Used for structural analysis and model figure generation [24]
13 PROCHECK’s Ramachandran plot analysis Used to analyze the quality and accuracy of the predicted 3D model structure [25]
14 Verify3D Used to assess protein’s model with 3D profiles [26]
15 ERRAT Used to analyze the statistics of non-bonded interactions between different atoms and verify protein structures [27]
F Functional annotation
16 CD Search Used to search for conserved structural and functional domains in a sequence [28]
17 InterProScan Used to search interPro for motif discovery [29]
G Protein-protein interaction
18 STRING Used for predicting protein-protein interaction [10]
H Active site identification
19 CASTp Used to find, outline, and estimate inward surface regions on protein 3D structure [30]
Table 2.
Similar protein obtained from non-redundant protein sequences (nr) database
Protein name Source organism Accession ID Identity (%) Score e-value
MULTISPECIES: MmgE/PrpD family protein Campylobacter WP_002866694.1 100 910 0
MmgE/PrpD family protein C. jejuni EHD2634150.1 99.78 909 0
MmgE/PrpD family protein C. jejuni WP_057100379.1 99.78 909 0
MmgE/PrpD family protein C. coli WP_193228049.1 99.55 908 0
MULTISPECIES: MmgE/PrpD family protein Campylobacter WP_002877370.1 99.78 908 0
Table 3.
Similar protein obtained from UniProtKB/Swiss-Prot (swissprot) database
Protein name Source organism Accession ID Identity (%) Score e-value
Cis-aconitate decarboxylase Mus musculus P54987.2 27.06 133 5e-33
Cis-aconitate decarboxylase Homo sapiens A6NK06.1 26.91 130 6e-32
Uncharacterized protein YxeQ Bacillus subtilis subsp. subtilis str. 168 P54956.2 23.81 128 2e-31
Cis-aconitate decarboxylase Aspergillus terreus B3IUN8.1 25.49 114 2e-26
Cis-aconitate decarboxylase Aspergillus terreus NIH2624 Q0C8L3.1 25.98 113 7e-26
Table 4.
ROC results of various tools and databases used in the present study
Tools name Accuracy of prediction (%) Sensitivity (%) Specificity (%) ROC area
BLAST 97.5 97.4 100 0.99
CD Search 95 94.9 100 0.99
InterProScan 97.5 97.4 100 0.99
Average 96.7 96.6 100 0.99

ROC, receiver operating characteristic.

References

1. Altekruse SF, Stern NJ, Fields PI, Swerdlow DL. Campylobacter jejuni: an emerging foodborne pathogen. Emerg Infect Dis 1999;5:28–35.
crossref pmid pmc
2. Balaban M, Hendrixson DR. Polar flagellar biosynthesis and a regulator of flagellar number influence spatial parameters of cell division in Campylobacter jejuni. PLoS Pathog 2011;7:e1002420.
crossref pmid pmc
3. Young KT, Davis LM, Dirita VJ. Campylobacter jejuni: molecular biology and pathogenesis. Nat Rev Microbiol 2007;5:665–679.
crossref pmid
4. Allos BM. Campylobacter jejuni Infections: update on emerging issues and trends. Clin Infect Dis 2001;32:1201–1206.
crossref pmid
5. Snelling WJ, Matsuda M, Moore JE, Dooley JS. Campylobacter jejuni. Lett Appl Microbiol 2005;41:297–302.
crossref pmid
6. Parkhill J, Wren BW, Mungall K, Ketley JM, Churcher C, Basham D, et al. The genome sequence of the food-borne pathogen Campylobacter jejuni reveals hypervariable sequences. Nature 2000;403:665–668.
crossref pmid
7. Jaroszewski L, Li Z, Krishna SS, Bakolitsa C, Wooley J, Deacon AM, et al. Exploration of uncharted regions of the protein universe. PLoS Biol 2009;7:e1000205.
crossref pmid pmc
8. Nimrod G, Schushan M, Steinberg DM, Ben-Tal N. Detection of functionally important regions in "hypothetical proteins" of known structure. Structure 2008;16:1755–1763.
crossref pmid
9. Ferdous N, Reza MN, Emon MT, Islam MS, Mohiuddin AK, Hossain MU. Molecular characterization and functional annotation of a hypothetical protein (SCO0618) of Streptomyces coelicolor A3(2). Genomics Inform 2020;18:e28.
crossref pmid pmc
10. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2019;47:D607–D613.
crossref pmid
11. Jez JM. Revisiting protein structure, function, and evolution in the genomic era. J Invertebr Pathol 2017;142:11–15.
crossref pmid
12. Stahl M, Butcher J, Stintzi A. Nutrient acquisition and metabolism by Campylobacter jejuni. Front Cell Infect Microbiol 2012;2:5.
crossref pmid pmc
13. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997;25:3389–3402.
crossref pmid pmc
14. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 2004;32:1792–1797.
crossref pmid pmc
15. Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol 2018;35:1547–1549.
crossref pmid pmc
16. Gasteiger E, Gattiker A, Hoogland C, Ivanyi I, Appel RD, Bairoch A. ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res 2003;31:3784–3788.
crossref pmid pmc
17. Yu CS, Lin CJ, Hwang JK. Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions. Protein Sci 2004;13:1402–1406.
crossref pmid pmc
18. Bhasin M, Garg A, Raghava GP. PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics 2005;21:2522–2524.
crossref pmid
19. Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 2010;26:1608–1615.
crossref pmid pmc
20. Secondary structure analysis of a protein using SOPMA. Ettimadai: Amrita Vishwa Vidyapeetham Virtual Lab, 2012. Accessed 2021 Nov 30. Available from: https://vlab.amrita.edu/?sub=3&brch=275&sim=1454&cnt=1.

21. Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 1999;292:195–202.
crossref pmid
22. Zimmermann L, Stephens A, Nam SZ, Rau D, Kubler J, Lozajic M, et al. A completely reimplemented MPI bioinformatics toolkit with a new HHpred server at its core. J Mol Biol 2018;430:2237–2243.
crossref pmid
23. Krieger E, Joo K, Lee J, Lee J, Raman S, Thompson J, et al. Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: four approaches that performed well in CASP8. Proteins 2009;77 Suppl 9:114–122.
crossref pmid
24. Likova E, Petkov P, Ilieva N, Litov L. The PyMOL Molecular Graphics System, version 2.0. New York: Schrödinger, LLC, 2015.

25. Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 1993;26:283–291.
crossref
26. Eisenberg D, Luthy R, Bowie JU. VERIFY3D: assessment of protein models with three-dimensional profiles. Methods Enzymol 1997;277:396–404.
pmid
27. Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 1993;2:1511–1519.
crossref pmid pmc
28. Marchler-Bauer A, Lu S, Anderson JB, Chitsaz F, Derbyshire MK, DeWeese-Scott C, et al. CDD: a conserved domain database for the functional annotation of proteins. Nucleic Acids Res 2011;39:D225–D229.
crossref pmid
29. Jones P, Binns D, Chang HY, Fraser M, Li W, McAnulla C, et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 2014;30:1236–1240.
crossref pmid pmc
30. Tian W, Chen C, Lei X, Zhao J, Liang J. CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Res 2018;46:W363–W367.
crossref pmid pmc
31. Eng J. ROC analysis: web-based calculator for ROC curves. Baltimore: Johns Hopkins University, 2014. Accessed 2021 Nov 30. Available from: http://www.jrocfit.org.

32. Halarnkar PP, Blomquist GJ. Comparative aspects of propionate metabolism. Comp Biochem Physiol B 1989;92:227–231.
crossref pmid
33. Suvorova IA, Ravcheev DA, Gelfand MS. Regulation and evolution of malonate and propionate catabolism in proteobacteria. J Bacteriol 2012;194:3234–3240.
crossref pmid pmc
34. Horswill AR, Escalante-Semerena JC. In vitro conversion of propionate to pyruvate by Salmonella enterica enzymes: 2-methylcitrate dehydratase (PrpD) and aconitase enzymes catalyze the conversion of 2-methylcitrate to 2-methylisocitrate. Biochemistry 2001;40:4703–4713.
crossref pmid
35. Blank L, Green J, Guest JR. AcnC of Escherichia coli is a 2-methylcitrate dehydratase (PrpD) that can use citrate and isocitrate as substrates. Microbiology (Reading) 2002;148:133–146.
crossref pmid
36. Lohkamp B, Bauerle B, Rieger PG, Schneider G. Three-dimensional structure of iminodisuccinate epimerase defines the fold of the MmgE/PrpD protein family. J Mol Biol 2006;362:555–566.
crossref pmid
37. Kanamasa S, Dwiarti L, Okabe M, Park EY. Cloning and functional characterization of the cis-aconitic acid decarboxylase (CAD) gene from Aspergillus terreus. Appl Microbiol Biotechnol 2008;80:223–229.
crossref pmid
38. Reddick JJ, Sirkisoon S, Dahal RA, Hardesty G, Hage NE, Booth WT, et al. First biochemical characterization of a methylcitric acid cycle from Bacillus subtilis strain 168. Biochemistry 2017;56:5698–5711.
crossref pmid


ABOUT
ARTICLE CATEGORY

Browse all articles >

BROWSE ARTICLES
FOR CONTRIBUTORS
Editorial Office
Room No. 806, 193 Mallijae-ro, Jung-gu, Seoul 04501, Korea
Tel: +82-2-558-9394    Fax: +82-2-558-9434    E-mail: kogo3@kogo.or.kr                

Copyright © 2022 by Korea Genome Organization.

Developed in M2PI

Close layer
prev next